Microprocessor-based electrical power equipment such as power monitors, lighting control systems, meters, circuit breaker controllers and the like accumulate considerable amounts of information concerning the electrical distribution systems to which they are connected, as well as the power equipment itself. The amount of such information generally increases over the operating life of the equipment, not only because of the retention of required historical information but also because of the increasingly complex functions being performed by such equipment. The information from the power monitoring systems and the like can be stored in a database. By querying the database, certain categories of data can be requested from the database, which is interpreted or manipulated to produce useful reports or other informational tools for identifying patterns, problems, or other anomalies in a particular set of data. As the amount of data increases, the database response times increase because there is more data that must be searched to ensure a complete result. Much of that data may be irrelevant or not important for the particular query. For example, historical data older than three months may not be relevant to a query for data representing the currents and voltages at a particular metering point. Yet, the presence of such “stale” or aging historical data poses an impediment to the database search engine, rendering it sluggish and slower to respond.
Slow database response times results in various disadvantages. Operators querying for data could lose patience with the lengthy response time and slow performance associated with data requests, and, as a result may actually be discouraged from running such queries in a manner that adversely affects operations and productivity. In addition, as databases become filled with data that may be disjointed, disorganized, and enormous in size, more sophisticated processes are required in order to extract and analyze the data. The process of searching large amounts of data for patterns is typically referred to as data mining. There exists sophisticated commercial tools for data mining, such as OLAP (on line analytical processing) tools, but while these tools can enhance database performance, they are expensive, complex, and bug-susceptible, which increases overall complexity of designing, maintaining, and interacting with the database system.
Performance is also adversely affected with respect to archiving data. Over time, performance begins to decrease as the bulk of data stored in a database table increases. To archive old data, it must be retrieved, saved to an external file, and removed from the database table. After the archived data is removed, the database server must reorganize the remaining data, which also results in performance decrease.
Thus, a need exists for an improved apparatus and method. The present invention is directed to satisfying one or more of these needs and solving other problems.